Overview

Dataset statistics

Number of variables17
Number of observations68205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 MiB
Average record size in memory136.0 B

Variable types

Numeric8
Categorical9

Alerts

age is highly overall correlated with age_yearsHigh correlation
weight is highly overall correlated with bmiHigh correlation
ap_hi is highly overall correlated with ap_lo and 2 other fieldsHigh correlation
ap_lo is highly overall correlated with ap_hi and 2 other fieldsHigh correlation
age_years is highly overall correlated with ageHigh correlation
bmi is highly overall correlated with weightHigh correlation
bp_category is highly overall correlated with ap_hi and 2 other fieldsHigh correlation
bp_category_encoded is highly overall correlated with ap_hi and 2 other fieldsHigh correlation
gluc is highly imbalanced (52.2%)Imbalance
smoke is highly imbalanced (57.1%)Imbalance
alco is highly imbalanced (70.0%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-10-05 02:56:38.336999
Analysis finished2023-10-05 02:57:10.125560
Duration31.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct68205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49972.41
Minimum0
Maximum99999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size533.0 KiB
2023-10-05T08:27:10.466726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4952.2
Q124991
median50008
Q374878
95-th percentile94933.4
Maximum99999
Range99999
Interquartile range (IQR)49887

Descriptive statistics

Standard deviation28852.138
Coefficient of variation (CV)0.57736135
Kurtosis-1.1983614
Mean49972.41
Median Absolute Deviation (MAD)24949
Skewness-0.0014792948
Sum3.4083683 × 109
Variance8.3244588 × 108
MonotonicityStrictly increasing
2023-10-05T08:27:10.910502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
66648 1
 
< 0.1%
66624 1
 
< 0.1%
66625 1
 
< 0.1%
66626 1
 
< 0.1%
66628 1
 
< 0.1%
66630 1
 
< 0.1%
66631 1
 
< 0.1%
66632 1
 
< 0.1%
66633 1
 
< 0.1%
Other values (68195) 68195
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
99999 1
< 0.1%
99998 1
< 0.1%
99996 1
< 0.1%
99995 1
< 0.1%
99993 1
< 0.1%
99992 1
< 0.1%
99991 1
< 0.1%
99990 1
< 0.1%
99988 1
< 0.1%
99986 1
< 0.1%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct8061
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19462.668
Minimum10798
Maximum23713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 KiB
2023-10-05T08:27:11.361121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10798
5-th percentile15054.2
Q117656
median19700
Q321323
95-th percentile23256
Maximum23713
Range12915
Interquartile range (IQR)3667

Descriptive statistics

Standard deviation2468.3819
Coefficient of variation (CV)0.12682649
Kurtosis-0.82605314
Mean19462.668
Median Absolute Deviation (MAD)1713
Skewness-0.3048272
Sum1.3274513 × 109
Variance6092909
MonotonicityNot monotonic
2023-10-05T08:27:11.795768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19741 32
 
< 0.1%
18253 31
 
< 0.1%
21892 30
 
< 0.1%
18236 30
 
< 0.1%
18184 30
 
< 0.1%
20442 29
 
< 0.1%
19733 29
 
< 0.1%
20376 29
 
< 0.1%
20389 29
 
< 0.1%
21159 28
 
< 0.1%
Other values (8051) 67908
99.6%
ValueCountFrequency (%)
10798 1
 
< 0.1%
10859 1
 
< 0.1%
10878 1
 
< 0.1%
10964 1
 
< 0.1%
14275 1
 
< 0.1%
14277 1
 
< 0.1%
14282 1
 
< 0.1%
14284 1
 
< 0.1%
14287 1
 
< 0.1%
14291 3
< 0.1%
ValueCountFrequency (%)
23713 1
< 0.1%
23701 1
< 0.1%
23692 1
< 0.1%
23690 1
< 0.1%
23687 1
< 0.1%
23684 1
< 0.1%
23678 1
< 0.1%
23677 1
< 0.1%
23675 2
< 0.1%
23673 2
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
1
44427 
2
23778 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 44427
65.1%
2 23778
34.9%

Length

2023-10-05T08:27:12.196754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:12.577534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 44427
65.1%
2 23778
34.9%

Most occurring characters

ValueCountFrequency (%)
1 44427
65.1%
2 23778
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68205
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44427
65.1%
2 23778
34.9%

Most occurring scripts

ValueCountFrequency (%)
Common 68205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44427
65.1%
2 23778
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44427
65.1%
2 23778
34.9%

height
Real number (ℝ)

Distinct106
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.37286
Minimum55
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 KiB
2023-10-05T08:27:13.060820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile152
Q1159
median165
Q3170
95-th percentile178
Maximum250
Range195
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.1767564
Coefficient of variation (CV)0.049745173
Kurtosis7.6469704
Mean164.37286
Median Absolute Deviation (MAD)5
Skewness-0.61186845
Sum11211051
Variance66.859345
MonotonicityNot monotonic
2023-10-05T08:27:13.627126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 5728
 
8.4%
160 4890
 
7.2%
170 4584
 
6.7%
168 4307
 
6.3%
164 3323
 
4.9%
158 3236
 
4.7%
162 3180
 
4.7%
169 2741
 
4.0%
156 2681
 
3.9%
167 2486
 
3.6%
Other values (96) 31049
45.5%
ValueCountFrequency (%)
55 1
 
< 0.1%
57 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
64 1
 
< 0.1%
65 2
< 0.1%
67 3
< 0.1%
68 2
< 0.1%
70 2
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
250 1
 
< 0.1%
207 1
 
< 0.1%
198 14
< 0.1%
197 4
 
< 0.1%
196 6
< 0.1%
195 6
< 0.1%
194 2
 
< 0.1%
193 6
< 0.1%
192 12
< 0.1%
191 11
< 0.1%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct278
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.100688
Minimum11
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 KiB
2023-10-05T08:27:14.114040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile55
Q165
median72
Q382
95-th percentile100
Maximum200
Range189
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.288862
Coefficient of variation (CV)0.19283036
Kurtosis2.5571391
Mean74.100688
Median Absolute Deviation (MAD)8
Skewness1.0058102
Sum5054037.4
Variance204.17158
MonotonicityNot monotonic
2023-10-05T08:27:14.546753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 3779
 
5.5%
70 3692
 
5.4%
68 2767
 
4.1%
75 2675
 
3.9%
60 2670
 
3.9%
80 2569
 
3.8%
72 2249
 
3.3%
69 2152
 
3.2%
78 2035
 
3.0%
74 1827
 
2.7%
Other values (268) 41790
61.3%
ValueCountFrequency (%)
11 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
28 1
 
< 0.1%
29 1
 
< 0.1%
30 3
< 0.1%
31 1
 
< 0.1%
32 3
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
200 2
< 0.1%
183 1
 
< 0.1%
180 4
< 0.1%
178 3
< 0.1%
177 1
 
< 0.1%
172 1
 
< 0.1%
171 1
 
< 0.1%
170 3
< 0.1%
169 1
 
< 0.1%
168 3
< 0.1%

ap_hi
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.43492
Minimum90
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 KiB
2023-10-05T08:27:14.996647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile100
Q1120
median120
Q3140
95-th percentile160
Maximum180
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.961685
Coefficient of variation (CV)0.12624427
Kurtosis0.76049898
Mean126.43492
Median Absolute Deviation (MAD)10
Skewness0.73995679
Sum8623494
Variance254.77539
MonotonicityNot monotonic
2023-10-05T08:27:15.307122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 27654
40.5%
140 9324
 
13.7%
130 8907
 
13.1%
110 8617
 
12.6%
150 4197
 
6.2%
160 2792
 
4.1%
100 2563
 
3.8%
90 928
 
1.4%
170 647
 
0.9%
180 602
 
0.9%
Other values (76) 1974
 
2.9%
ValueCountFrequency (%)
90 928
 
1.4%
93 1
 
< 0.1%
95 28
 
< 0.1%
96 2
 
< 0.1%
99 4
 
< 0.1%
100 2563
3.8%
101 4
 
< 0.1%
102 8
 
< 0.1%
103 9
 
< 0.1%
104 6
 
< 0.1%
ValueCountFrequency (%)
180 602
0.9%
179 4
 
< 0.1%
178 2
 
< 0.1%
177 2
 
< 0.1%
176 3
 
< 0.1%
175 14
 
< 0.1%
174 3
 
< 0.1%
173 2
 
< 0.1%
172 8
 
< 0.1%
171 8
 
< 0.1%

ap_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.263925
Minimum60
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 KiB
2023-10-05T08:27:15.682978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile70
Q180
median80
Q390
95-th percentile100
Maximum120
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1439848
Coefficient of variation (CV)0.11252207
Kurtosis0.93192798
Mean81.263925
Median Absolute Deviation (MAD)0
Skewness0.23882217
Sum5542606
Variance83.612458
MonotonicityNot monotonic
2023-10-05T08:27:15.996623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 34725
50.9%
90 14239
20.9%
70 10212
 
15.0%
100 3978
 
5.8%
60 2656
 
3.9%
79 357
 
0.5%
110 338
 
0.5%
85 290
 
0.4%
75 209
 
0.3%
95 158
 
0.2%
Other values (48) 1043
 
1.5%
ValueCountFrequency (%)
60 2656
3.9%
61 6
 
< 0.1%
62 7
 
< 0.1%
63 7
 
< 0.1%
64 10
 
< 0.1%
65 78
 
0.1%
66 11
 
< 0.1%
67 19
 
< 0.1%
68 13
 
< 0.1%
69 98
 
0.1%
ValueCountFrequency (%)
120 134
 
0.2%
119 2
 
< 0.1%
115 7
 
< 0.1%
114 1
 
< 0.1%
113 3
 
< 0.1%
112 1
 
< 0.1%
111 1
 
< 0.1%
110 338
0.5%
109 6
 
< 0.1%
108 3
 
< 0.1%

cholesterol
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
1
51222 
2
9191 
3
7792 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68205
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 51222
75.1%
2 9191
 
13.5%
3 7792
 
11.4%

Length

2023-10-05T08:27:16.296944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:16.546638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 51222
75.1%
2 9191
 
13.5%
3 7792
 
11.4%

Most occurring characters

ValueCountFrequency (%)
1 51222
75.1%
2 9191
 
13.5%
3 7792
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68205
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 51222
75.1%
2 9191
 
13.5%
3 7792
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 68205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 51222
75.1%
2 9191
 
13.5%
3 7792
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 51222
75.1%
2 9191
 
13.5%
3 7792
 
11.4%

gluc
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
1
58027 
3
 
5180
2
 
4998

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68205
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 58027
85.1%
3 5180
 
7.6%
2 4998
 
7.3%

Length

2023-10-05T08:27:16.753384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:16.996722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 58027
85.1%
3 5180
 
7.6%
2 4998
 
7.3%

Most occurring characters

ValueCountFrequency (%)
1 58027
85.1%
3 5180
 
7.6%
2 4998
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68205
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 58027
85.1%
3 5180
 
7.6%
2 4998
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 68205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 58027
85.1%
3 5180
 
7.6%
2 4998
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 58027
85.1%
3 5180
 
7.6%
2 4998
 
7.3%

smoke
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
0
62226 
1
 
5979

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62226
91.2%
1 5979
 
8.8%

Length

2023-10-05T08:27:17.204107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:17.405744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 62226
91.2%
1 5979
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 62226
91.2%
1 5979
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68205
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62226
91.2%
1 5979
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 68205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62226
91.2%
1 5979
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62226
91.2%
1 5979
 
8.8%

alco
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
0
64581 
1
 
3624

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64581
94.7%
1 3624
 
5.3%

Length

2023-10-05T08:27:17.606811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:17.836296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 64581
94.7%
1 3624
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 64581
94.7%
1 3624
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68205
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64581
94.7%
1 3624
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 68205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64581
94.7%
1 3624
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64581
94.7%
1 3624
 
5.3%

active
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
1
54806 
0
13399 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 54806
80.4%
0 13399
 
19.6%

Length

2023-10-05T08:27:18.026582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:18.260293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 54806
80.4%
0 13399
 
19.6%

Most occurring characters

ValueCountFrequency (%)
1 54806
80.4%
0 13399
 
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68205
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 54806
80.4%
0 13399
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 68205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 54806
80.4%
0 13399
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54806
80.4%
0 13399
 
19.6%

cardio
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
0
34533 
1
33672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 34533
50.6%
1 33672
49.4%

Length

2023-10-05T08:27:18.473790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:18.706591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34533
50.6%
1 33672
49.4%

Most occurring characters

ValueCountFrequency (%)
0 34533
50.6%
1 33672
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68205
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34533
50.6%
1 33672
49.4%

Most occurring scripts

ValueCountFrequency (%)
Common 68205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34533
50.6%
1 33672
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34533
50.6%
1 33672
49.4%

age_years
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.823635
Minimum29
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 KiB
2023-10-05T08:27:18.919678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile41
Q148
median53
Q358
95-th percentile63
Maximum64
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7699095
Coefficient of variation (CV)0.12816061
Kurtosis-0.82138264
Mean52.823635
Median Absolute Deviation (MAD)5
Skewness-0.30356736
Sum3602836
Variance45.831674
MonotonicityNot monotonic
2023-10-05T08:27:19.220014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
55 3825
 
5.6%
53 3751
 
5.5%
57 3568
 
5.2%
54 3530
 
5.2%
56 3507
 
5.1%
59 3484
 
5.1%
49 3336
 
4.9%
58 3312
 
4.9%
51 3274
 
4.8%
52 3194
 
4.7%
Other values (18) 33424
49.0%
ValueCountFrequency (%)
29 3
 
< 0.1%
30 1
 
< 0.1%
39 1749
2.6%
40 1591
2.3%
41 1855
2.7%
42 1390
2.0%
43 1981
2.9%
44 1475
2.2%
45 2039
3.0%
46 1594
2.3%
ValueCountFrequency (%)
64 2122
3.1%
63 2652
3.9%
62 2135
3.1%
61 2647
3.9%
60 3127
4.6%
59 3484
5.1%
58 3312
4.9%
57 3568
5.2%
56 3507
5.1%
55 3825
5.6%

bmi
Real number (ℝ)

HIGH CORRELATION 

Distinct3752
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.510513
Minimum3.4717839
Maximum298.66667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 KiB
2023-10-05T08:27:19.663793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.4717839
5-th percentile20.936639
Q123.875115
median26.346494
Q330.116213
95-th percentile37.253489
Maximum298.66667
Range295.19488
Interquartile range (IQR)6.2410984

Descriptive statistics

Standard deviation6.026497
Coefficient of variation (CV)0.2190616
Kurtosis230.5905
Mean27.510513
Median Absolute Deviation (MAD)2.9229366
Skewness7.8187178
Sum1876354.6
Variance36.318667
MonotonicityNot monotonic
2023-10-05T08:27:20.094131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.87511478 930
 
1.4%
23.4375 641
 
0.9%
24.22145329 485
 
0.7%
25.71166208 359
 
0.5%
22.03856749 354
 
0.5%
23.03004535 342
 
0.5%
24.8015873 325
 
0.5%
23.52941176 313
 
0.5%
24.97704316 284
 
0.4%
25.390625 279
 
0.4%
Other values (3742) 63893
93.7%
ValueCountFrequency (%)
3.471783866 1
< 0.1%
7.022247758 1
< 0.1%
8.001828989 1
< 0.1%
9.331007343 1
< 0.1%
9.917581478 1
< 0.1%
10.7266436 1
< 0.1%
11.71875 1
< 0.1%
12.25447288 1
< 0.1%
12.85583104 1
< 0.1%
13.49300051 1
< 0.1%
ValueCountFrequency (%)
298.6666667 1
< 0.1%
278.125 1
< 0.1%
267.768595 1
< 0.1%
237.7686328 1
< 0.1%
191.6666667 1
< 0.1%
187.7500769 1
< 0.1%
180.6780742 1
< 0.1%
178.9627465 1
< 0.1%
178.2134106 1
< 0.1%
170.4142012 1
< 0.1%

bp_category
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
Hypertension Stage 1
39750 
Hypertension Stage 2
15937 
Normal
9417 
Elevated
 
3101

Length

Max length20
Median length20
Mean length17.521443
Min length6

Characters and Unicode

Total characters1195050
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHypertension Stage 1
2nd rowHypertension Stage 2
3rd rowHypertension Stage 1
4th rowHypertension Stage 2
5th rowNormal

Common Values

ValueCountFrequency (%)
Hypertension Stage 1 39750
58.3%
Hypertension Stage 2 15937
23.4%
Normal 9417
 
13.8%
Elevated 3101
 
4.5%

Length

2023-10-05T08:27:20.525555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:20.883813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
hypertension 55687
31.0%
stage 55687
31.0%
1 39750
22.1%
2 15937
 
8.9%
normal 9417
 
5.2%
elevated 3101
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 173263
14.5%
t 114475
 
9.6%
111374
 
9.3%
n 111374
 
9.3%
a 68205
 
5.7%
r 65104
 
5.4%
o 65104
 
5.4%
H 55687
 
4.7%
g 55687
 
4.7%
y 55687
 
4.7%
Other values (12) 319090
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 904097
75.7%
Uppercase Letter 123892
 
10.4%
Space Separator 111374
 
9.3%
Decimal Number 55687
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 173263
19.2%
t 114475
12.7%
n 111374
12.3%
a 68205
 
7.5%
r 65104
 
7.2%
o 65104
 
7.2%
g 55687
 
6.2%
y 55687
 
6.2%
i 55687
 
6.2%
s 55687
 
6.2%
Other values (5) 83824
9.3%
Uppercase Letter
ValueCountFrequency (%)
H 55687
44.9%
S 55687
44.9%
N 9417
 
7.6%
E 3101
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 39750
71.4%
2 15937
28.6%
Space Separator
ValueCountFrequency (%)
111374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1027989
86.0%
Common 167061
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 173263
16.9%
t 114475
11.1%
n 111374
10.8%
a 68205
 
6.6%
r 65104
 
6.3%
o 65104
 
6.3%
H 55687
 
5.4%
g 55687
 
5.4%
y 55687
 
5.4%
S 55687
 
5.4%
Other values (9) 207716
20.2%
Common
ValueCountFrequency (%)
111374
66.7%
1 39750
 
23.8%
2 15937
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1195050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 173263
14.5%
t 114475
 
9.6%
111374
 
9.3%
n 111374
 
9.3%
a 68205
 
5.7%
r 65104
 
5.4%
o 65104
 
5.4%
H 55687
 
4.7%
g 55687
 
4.7%
y 55687
 
4.7%
Other values (12) 319090
26.7%

bp_category_encoded
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size533.0 KiB
Hypertension Stage 1
39750 
Hypertension Stage 2
15937 
Normal
9417 
Elevated
 
3101

Length

Max length20
Median length20
Mean length17.521443
Min length6

Characters and Unicode

Total characters1195050
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHypertension Stage 1
2nd rowHypertension Stage 2
3rd rowHypertension Stage 1
4th rowHypertension Stage 2
5th rowNormal

Common Values

ValueCountFrequency (%)
Hypertension Stage 1 39750
58.3%
Hypertension Stage 2 15937
23.4%
Normal 9417
 
13.8%
Elevated 3101
 
4.5%

Length

2023-10-05T08:27:21.226701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T08:27:21.586742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
hypertension 55687
31.0%
stage 55687
31.0%
1 39750
22.1%
2 15937
 
8.9%
normal 9417
 
5.2%
elevated 3101
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 173263
14.5%
t 114475
 
9.6%
111374
 
9.3%
n 111374
 
9.3%
a 68205
 
5.7%
r 65104
 
5.4%
o 65104
 
5.4%
H 55687
 
4.7%
g 55687
 
4.7%
y 55687
 
4.7%
Other values (12) 319090
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 904097
75.7%
Uppercase Letter 123892
 
10.4%
Space Separator 111374
 
9.3%
Decimal Number 55687
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 173263
19.2%
t 114475
12.7%
n 111374
12.3%
a 68205
 
7.5%
r 65104
 
7.2%
o 65104
 
7.2%
g 55687
 
6.2%
y 55687
 
6.2%
i 55687
 
6.2%
s 55687
 
6.2%
Other values (5) 83824
9.3%
Uppercase Letter
ValueCountFrequency (%)
H 55687
44.9%
S 55687
44.9%
N 9417
 
7.6%
E 3101
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 39750
71.4%
2 15937
28.6%
Space Separator
ValueCountFrequency (%)
111374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1027989
86.0%
Common 167061
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 173263
16.9%
t 114475
11.1%
n 111374
10.8%
a 68205
 
6.6%
r 65104
 
6.3%
o 65104
 
6.3%
H 55687
 
5.4%
g 55687
 
5.4%
y 55687
 
5.4%
S 55687
 
5.4%
Other values (9) 207716
20.2%
Common
ValueCountFrequency (%)
111374
66.7%
1 39750
 
23.8%
2 15937
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1195050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 173263
14.5%
t 114475
 
9.6%
111374
 
9.3%
n 111374
 
9.3%
a 68205
 
5.7%
r 65104
 
5.4%
o 65104
 
5.4%
H 55687
 
4.7%
g 55687
 
4.7%
y 55687
 
4.7%
Other values (12) 319090
26.7%

Interactions

2023-10-05T08:27:05.306858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:46.045706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:48.575775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:51.613311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:54.636986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:57.611178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:00.029146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:02.515671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:05.656763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:46.303593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:48.923877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:51.974223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:54.995052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:57.945001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:00.296593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:02.768658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:06.017325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:46.554072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:49.286817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:52.362465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:55.360082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:58.296704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:00.576811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:03.063842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:06.406767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:46.887288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:49.684961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:52.754091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:55.754917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:58.675743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:00.844142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:03.433949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:06.799701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:47.183371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:50.066353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:53.136959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:56.133951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:59.026690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:01.503594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:03.845071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:07.137827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:47.436972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:50.468823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:53.486603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:56.477915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:59.279704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:01.752961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:04.196124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:07.492902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:47.786902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:50.846852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:53.866824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:56.849208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:59.534517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:02.004954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:04.555856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:07.865949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:48.199733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:51.218006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:54.261133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:57.240174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:26:59.778339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:02.262229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-05T08:27:04.945077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-05T08:27:21.959487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idageheightweightap_hiap_loage_yearsbmigendercholesterolglucsmokealcoactivecardiobp_categorybp_category_encoded
id1.0000.003-0.002-0.0020.003-0.0010.003-0.0010.0130.0060.0000.0040.0000.0060.0060.0060.006
age0.0031.000-0.0820.0610.2220.1570.9990.1070.0520.1130.0710.0480.0280.0140.2400.1120.112
height-0.002-0.0821.0000.3140.0210.031-0.084-0.1830.4150.0310.0120.1690.0890.0140.0170.0370.037
weight-0.0020.0610.3141.0000.2760.2490.0630.8480.1690.0960.0820.0680.0640.0170.1650.1350.135
ap_hi0.0030.2220.0210.2761.0000.7410.2230.2780.0870.1740.0890.0310.0380.0210.4630.6780.678
ap_lo-0.0010.1570.0310.2490.7411.0000.1580.2440.0720.1310.0650.0250.0450.0090.3650.7230.723
age_years0.0030.999-0.0840.0630.2230.1581.0000.1100.0510.1120.0700.0480.0290.0140.2400.1120.112
bmi-0.0010.107-0.1830.8480.2780.2440.1101.0000.1150.0930.0720.0310.0000.0200.1310.0860.086
gender0.0130.0520.4150.1690.0870.0720.0510.1151.0000.0370.0210.3380.1710.0030.0050.0800.080
cholesterol0.0060.1130.0310.0960.1740.1310.1120.0930.0371.0000.3930.0240.0430.0120.2210.1220.122
gluc0.0000.0710.0120.0820.0890.0650.0700.0720.0210.3931.0000.0190.0290.0110.0910.0630.063
smoke0.0040.0480.1690.0680.0310.0250.0480.0310.3380.0240.0191.0000.3380.0250.0160.0200.020
alco0.0000.0280.0890.0640.0380.0450.0290.0000.1710.0430.0290.3381.0000.0240.0080.0300.030
active0.0060.0140.0140.0170.0210.0090.0140.0200.0030.0120.0110.0250.0241.0000.0380.0140.014
cardio0.0060.2400.0170.1650.4630.3650.2400.1310.0050.2210.0910.0160.0080.0381.0000.3730.373
bp_category0.0060.1120.0370.1350.6780.7230.1120.0860.0800.1220.0630.0200.0300.0140.3731.0001.000
bp_category_encoded0.0060.1120.0370.1350.6780.7230.1120.0860.0800.1220.0630.0200.0300.0140.3731.0001.000

Missing values

2023-10-05T08:27:08.436716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-05T08:27:09.412797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idagegenderheightweightap_hiap_locholesterolglucsmokealcoactivecardioage_yearsbmibp_categorybp_category_encoded
0018393216862.0110801100105021.967120Hypertension Stage 1Hypertension Stage 1
1120228115685.0140903100115534.927679Hypertension Stage 2Hypertension Stage 2
2218857116564.0130703100015123.507805Hypertension Stage 1Hypertension Stage 1
3317623216982.01501001100114828.710479Hypertension Stage 2Hypertension Stage 2
4417474115656.0100601100004723.011177NormalNormal
5821914115167.0120802200006029.384676Hypertension Stage 1Hypertension Stage 1
6922113115793.0130803100106037.729725Hypertension Stage 1Hypertension Stage 1
71222584217895.0130903300116129.983588Hypertension Stage 1Hypertension Stage 1
81317668115871.0110701100104828.440955NormalNormal
91419834116468.0110601100005425.282570NormalNormal
idagegenderheightweightap_hiap_locholesterolglucsmokealcoactivecardioage_yearsbmibp_categorybp_category_encoded
681959998615094116872.0110701100114125.510204NormalNormal
681969998820609115972.0130902200105628.479886Hypertension Stage 1Hypertension Stage 1
681979999018792116156.0170901100115121.604105Hypertension Stage 2Hypertension Stage 2
681989999119699117270.0130901100115323.661439Hypertension Stage 1Hypertension Stage 1
681999999221074116580.0150801100115729.384757Hypertension Stage 1Hypertension Stage 1
682009999319240216876.0120801110105226.927438Hypertension Stage 1Hypertension Stage 1
6820199995226011158126.0140902200116150.472681Hypertension Stage 2Hypertension Stage 2
6820299996190662183105.0180903101015231.353579Hypertension Stage 2Hypertension Stage 2
682039999822431116372.0135801200016127.099251Hypertension Stage 1Hypertension Stage 1
682049999920540117072.0120802100105624.913495Hypertension Stage 1Hypertension Stage 1